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Textbook Notes for Business Analytics at Hofstra University


HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 9: Normal Distribution, Standard Score, Standard Deviation

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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 2: Venn Diagram, Null Set, Empty Set

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Finite sets consist of a countable number of elements. Infinite sets consist of an uncountable number of elements. The set consisting of elements a, b,
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 9: Central Limit Theorem, Confidence Interval, Interval Estimation

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We want to estimate or make a statement about a population parameter - say the population mean or the population standard deviation. The way to do this
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 7: Binomial Distribution, Poisson Distribution

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Let us assume that we conduct an experiment that consists of counting the number off events that will occur in a specific amount of time, or in a speci
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter 2: Probabillity Problem Solution

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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 10: Confidence Interval, Interval Estimation, Xu

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Confidence intervals: confidence intervals for the mean population standard. We would like to estimate the value of from sample data. We begin with the
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 11: Minitab, In Essence, Null Hypothesis

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Hypothesis tests: hypothesis testing deals with making rational decisions when only incomplete information derived from a sample is available. Definiti
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 3: Sample Space, Fair Coin

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Outcomes all the results of an experiment which are of interest are called outcomes. Sample space the totality of all outcomes of an experiment is call
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 4: Standard Deviation, Frequency Distribution, Poisson Distribution

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We have previously used a strategy of enumeration and basic probability rules to calculate probabilities of outcomes and events. However, as the number
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 8: Abh, Probability Density Function, Probability Distribution

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The uniform probability distribution is a widely used distribution in sampling and simulation. We study it because it is the most simple continuous pro
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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 1: Variance, Xi Xi, Central Tendency

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HOFSTRA UBAN 001John AffiscoFall

BAN 001 Chapter Notes - Chapter 3.5: Conditional Probability, Sample Space, Mutual Exclusivity

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From our previous lecture we know that the union is of interest when the probability of any one of several events (or outcomes) occurring is required.
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